We know not to trust user input, but what about AI output? Learn how improper output handling leads to XSS, SQL injection, and RCE, and how to prevent it.

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LLM output should be treated as untrusted user input to prevent security vulnerabilities. When applications blindly trust AI-generated content without proper validation and sanitization, they become vulnerable to classic attacks like XSS, SQL injection, and remote code execution. The article demonstrates how prompt injection can manipulate LLMs into generating malicious payloads, then provides practical code examples showing vulnerable patterns and secure alternatives using techniques like parameterized queries, context-aware encoding, and the principle of least privilege.

Trusting AI Output? Why Improper Output Handling is the New XSS

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